Rolling your own reduceByKey in Spark Dataset

Question

I\'m trying to learn to use DataFrames and DataSets more in addition to RDDs. For an RDD, I know I can do someRDD.reduceByKey((x,y) => x + y), but I don\'t

Answer 1

A more efficient solution uses mapPartitions before groupByKey to reduce the amount of shuffling (note this is not the exact same signature as reduceByKey but I think it is more flexible to pass a function than require the dataset consist of a tuple).

def reduceByKey[V: ClassTag, K](ds: Dataset[V], f: V => K, g: (V, V) => V)
  (implicit encK: Encoder[K], encV: Encoder[V]): Dataset[(K, V)] = {
  def h[V: ClassTag, K](f: V => K, g: (V, V) => V, iter: Iterator[V]): Iterator[V] = {
    iter.toArray.groupBy(f).mapValues(_.reduce(g)).map(_._2).toIterator
  }
  ds.mapPartitions(h(f, g, _))
    .groupByKey(f)(encK)
    .reduceGroups(g)
}

Depending on the shape/size of your data, this is within 1 second of the performance of reduceByKey, and about 2x as fast as a groupByKey(_._1).reduceGroups. There is still room for improvements, so suggestions would be welcome.

Answer 2

I assume your goal is to translate this idiom to Datasets:

rdd.map(x => (x.someKey, x.someField))
   .reduceByKey(_ + _)

// => returning an RDD of (KeyType, FieldType)

Currently, the closest solution I have found with the Dataset API looks like this:

ds.map(x => (x.someKey, x.someField))          // [1]
  .groupByKey(_._1)                            
  .reduceGroups((a, b) => (a._1, a._2 + b._2))
  .map(_._2)                                   // [2]

// => returning a Dataset of (KeyType, FieldType)

// Comments:
// [1] As far as I can see, having a map before groupByKey is required
//     to end up with the proper type in reduceGroups. After all, we do
//     not want to reduce over the original type, but the FieldType.
// [2] required since reduceGroups converts back to Dataset[(K, V)]
//     not knowing that our V's are already key-value pairs.

Doesn't look very elegant and according to a quick benchmark it is also much less performant, so maybe we are missing something here...

Note: An alternative might be to use groupByKey(_.someKey) as a first step. The problem is that using groupByKey changes the type from a regular Dataset to a KeyValueGroupedDataset. The latter does not have a regular map function. Instead it offers an mapGroups, which does not seem very convenient because it wraps the values into an Iterator and performs a shuffle according to the docstring.